Not Applicable
1. Field of the Invention
The present invention relates managing consumption of utilities, such as electricity, natural gas and water; and more particularly to detecting the occurrence of abnormal usage.
2. Description of the Related Art
Large buildings often incorporate computerized control systems which manage the operation of different subsystems, such that for heating, ventilation and air conditioning. In addition to ensuring that the subsystem performs as desired, the control system operates the associated equipment in as efficiently as possible.
A large entity may have numerous buildings under common management, such as on a university campus or a chain of store located in different cities. To accomplish this, the controllers in each building gather data regarding performance of the building subsystems which data can be analyzed at the central monitoring location.
With the cost of energy increasing, building owners are looking for ways to conserve utility consumption. In addition, the cost of electricity for large consumers may be based on the peak use during a billing period. Thus high consumption of electricity during a single day can affect the rate at which the service is billed during an entire month. In addition, certain preferential rate plans require a customer to reduce consumption upon the request of the utility company, such as on days of large service demand throughout the entire utility distribution system. Failure to comply with the request usually results in stiff monetary penalties which raises the energy cost significantly above that for an unrestricted rate plan. Therefore, a consumer has to analyze the energy usage in order to determine the best rate plan and implement processes to ensure that operation of the facility does not inappropriately cause an increase in utility costs.
In addition, abnormal energy or other utility consumption may indicate malfunctioning equipment or other problems in the building. Therefore, monitoring utility usage and detecting abnormal consumption levels can indicate when maintenance or replacement of the machinery is required.
As a consequence, sensors are being incorporated into building management systems to measure utility usage for the entire building, as well as specific subsystems such as heating, air conditioning and ventilation equipment. These management systems collect and store massive quantities of utility use data, which is overwhelming to the facility operator when attempting to analyze that data in an effort to detect anomalies.
Alarm and warning systems and data visualization programs often are provided to assist in deriving meaningful information from the gathered data. However, human operators must select the thresholds for alarms and warnings, which is a daunting task. If the thresholds are too tight, then numerous false alarms are issued; and if the thresholds are too loose, equipment or system failures can go undetected. The data visualization programs can help building operators detect and diagnose problems, but a large amount of time can be spent detecting problems. Also, the expertise of building operators varies greatly. New or inexperienced operators may have difficulty detecting faults, and the performance of an operator may vary with the time of day or day of the week.
Therefore, there is a need for robust data analysis methods to automatically determine if the current energy use is significantly different than previous energy patterns and, if so, to alert the building operator or mechanics to investigate and correct the problem.
Abnormal utility usage by a building or a particular apparatus in the building can be determined by repeatedly measuring the level of use of the utility, thereby producing a plurality of utility measurements. A Generalized Extreme Studentized Deviate (GESD) statistical procedure is applied to the plurality of utility measurements to identify any measurement outliers. The measurement outliers denote times when unusual utility consumption occurred, thereby indicating times during which operation of the building or the particular apparatus should be investigated.
In the preferred embodiment, a severity of abnormal utility usage can be established by determining a degree to which the associated outlier deviates from the norm. This can be accomplished by calculating robust estimates of the mean ({overscore (x)}robust) and the standard deviation (srobust) of each outlier